In many image processing systems it is necessary that images be aligned with one another to perform image merging or analysis. The phrase image processing, as used herein, is intended to encompass the processing of all forms of images including temporally unrelated images as well as images (frames) of a video signal, i.e., a sequence of temporally related images. Image alignment in an image processing system is necessary to create mosaics of multiple images, perform some forms of image compression, perform motion estimation and/or tracking and the like. Alignment (also known as registration) of images begins with determining a displacement field that represents the offset between the images and then warping one image to the other to remove or minimize the offset. The images may be taken from the same sensor or from two entirely different sensors, possibly of different modality (e.g., an infrared sensor and a visible sensor). Often, the displacement field that defines the offset between the images can be described as a global parametric transformation between the two images, such as an affine, quadratic, or a projective transformation. Many techniques have been developed for the parametric transformation of a pair of images.
Most flow-based techniques divide the registration process into two steps: first a flow-field is estimated, then, using regression, the global parametric transformation which best describes the flow field is found. However, often the local flow estimates are noisy and unreliable, resulting in poor registration accuracy and a lack of robustness.
To overcome this problem, direct gradient-based techniques have been developed. These techniques estimate the global transformation parameters by directly using local image intensity information without first computing a local flow-field. They achieve high registration accuracy since they avoid the noisy step of computing a local flow-estimation. However, these techniques assume that the intensity values of corresponding pixels in the two images are the same, which is known as the "brightness constancy assumption". As a result, the applicability of direct gradient-based techniques is limited to situations when the images to be registered are substantially similar in appearance. Consequently, the direct gradient-based techniques cannot handle large changes in illumination and contrast between the images. Because the images need to be substantially similar to be registered using direct gradient-based techniques, images produced by sensors having different modality and/or containing a substantial range of motion cannot be accurately registered by direct gradient-based techniques.
Therefore, a need exists in the art for a method and apparatus that aligns images having substantial illumination differences between the images and/or a substantial amount of motion and/or other image differences that would otherwise make registration difficult.